Systems and methods for optimization of on-line adaptive radiation therapy

ABSTRACT

Methods and systems are disclosed for radiation treatment of a subject involving one or more fractional treatments. A fractional treatment comprises: obtaining fractional image data pertaining to a region of interest of the subject; performing a fractional optimization of a radiation treatment plan to determine optimized values of one or more radiation delivery variables based at least in part on the fractional image data; and delivering a fraction of the radiation treatment plan to the region of interest using the optimized values of the one or more radiation delivery variables as one or more corresponding parameters of the radiation treatment plan. A portion of performing the fractional optimization overlaps temporally with a portion of at least one of: obtaining the fractional image data and delivering the fraction of the radiation treatment plan.

RELATED APPLICATIONS

This application claims priority from U.S. patent application No.60/820,582 filed on 27 Jul. 2006, which is hereby incorporated herein byreference.

TECHNICAL FIELD

The invention relates to radiation therapy. Particular embodiments ofthe invention provide systems and methods for optimizing the delivery ofradiation dose to an individual.

BACKGROUND

Radiation therapy is used for various medical applications, such ascombating cancer, for example. Generally, speaking when irradiating asubject, it is desirable to impart a prescribed radiation dose to thediseased tissue (referred to as the “target” or “target volume”), whileminimizing (to the extent possible) the dose imparted to surroundinghealthy tissue and organs. Various systems and methods have been devisedfor delivering radiation while trying to achieve this objective. Suchsystems and methods generally involve: obtaining one or more images of aregion of interest (including the target volume) in the subject's body;initializing a radiation treatment plan; adapting or optimizingradiation delivery variables in effort to achieve the objectives of thetreatment plan; and delivering radiation. These procedures areillustrated in FIG. 1.

One drawback with current techniques is the time taken between theimaging procedure and completion of the radiation delivery procedure.The imaging procedure may involve obtaining a computed tomography (CT)image for example. The time between completing the imaging procedure andstarting the radiation delivery procedure may typically be on the orderof a week or two. Moreover, radiation delivery typically involvesseveral discrete steps referred to as “fractions”. By way of example, atreatment plan may be divided into 10 fractions and a subject mayreceive one fraction every day for 10 days. Thus, it may take on theorder of several weeks to a month (or more) between the imagingprocedure and completion of the radiation delivery procedure.

The characteristics of the target volume (e.g. the size, shape and/orlocation of the target volume) and the characteristics of the healthytissue (e.g. the size, shape and/or location of the healthy tissuerelative to the target volume) can change over time. By way ofnon-limiting example, a tumor in a subject's lung commonly moveswhenever the subject moves and a tumor in a subject's prostate may bedeformed by changes in the shape of the bladder and/or the rectum.Because the likelihood of changes in the characteristics of the targetvolume and/or the characteristics of the healthy tissue increases withtime, the time between imaging and radiation delivery represents asignificant limitation to the general desire of imparting a prescribedradiation dose to the target volume, while minimizing (to the extentpossible) the dose imparted to surrounding healthy tissue and organs.

Newer radiation delivery systems and methods referred to as “on-line”adaptive radiation therapy (ART) have attempted to reduce this timebetween the imaging and radiation delivery procedures. In on-line ARTtechniques, each of the FIG. 1 procedures is implemented for eachtreatment fraction. That is, for each fraction (i.e. each time that thesubject comes to the hospital), the subject is subjected to seriallyimplemented imaging, initializing, optimizing and radiation deliveryprocedures. Because on-line ART techniques involve a separate imagingprocedure (for each fraction) and radiation is delivered (for eachfraction) relatively soon after imaging, the characteristics of thetarget volume and the healthy tissue are less likely to change betweenthe imaging and radiation delivery procedures of each fraction.Accordingly, on-line ART has achieved some success at addressing thegeneral desire of imparting a prescribed radiation dose to the targetvolume, while minimizing (to the extent possible) the dose imparted tosurrounding healthy tissue and organs.

These gains achieved by on-line ART have not come without cost. Foron-line ART, the subject is typically required to be stationary on thetreatment couch (or at least in the treatment facility under the care ofmedical staff) for the entirety of each fraction (i.e. for eachiteration of the imaging, initializing, optimizing and radiationdelivery procedures). Accordingly, current on-line ART techniques areexpensive to implement because it takes a relatively long time toimplement each fraction. Treatment of each subject using on-line ARToccupies the radiation delivery system and other hospital resources(e.g. medical staff, rooms etc.) for a relatively large amount of time.In addition, the subject is required, for each fraction, to spend arelatively long time at the treatment facility which is generallyundesirable.

There is a general desire to reduce the amount of time required for eachiteration (i.e. each fraction) of on-line ART techniques.

SUMMARY

Aspects of the present invention provide methods and systems forradiation treatment.

One aspect of the invention provides a method for radiation treatment ofa subject involving one or more fractional treatments. A fractionaltreatment comprises: obtaining fractional image data pertaining to aregion of interest of the subject; performing a fractional optimizationof a radiation treatment plan to determine optimized values of one ormore radiation delivery variables based at least in part on thefractional image data; and delivering a fraction of the radiationtreatment plan to the region of interest using the optimized values ofthe one or more radiation delivery variables as one or morecorresponding parameters of the radiation treatment plan. A portion ofperforming the fractional optimization overlaps temporally with aportion of at least one of: obtaining the fractional image data anddelivering the fraction of the radiation treatment plan.

Another aspect of the invention provides a method for radiationtreatment of a subject involving one or more fractional treatments. Themethod involves obtaining initial image data pertaining to a region ofinterest of the subject and performing initial optimization of aninitial radiation treatment plan to determine optimized initial valuesof one or more initial variables based at least in part on the initialimage data. For at least one fractional treatment the method comprises:obtaining fractional image data pertaining to the region of interest ofthe subject; performing a fractional optimization of a radiationtreatment plan to determine optimized values of one or more radiationdelivery variables based at least in part on the fractional image data;and delivering a fraction of the radiation treatment plan to the regionof interest using the optimized values of the one or more radiationdelivery variables as one or more corresponding parameters of theradiation treatment plan. Performing the initial optimization comprisesusing a first optimization technique and performing the fractionaloptimization comprises using a second optimization technique. The firstand second optimization techniques differ from one another.

Other aspects of the invention provide computer program products andsystems for implementing the inventive methods disclosed herein.

Further aspects of the invention, features of specific embodiments ofthe invention and applications of the invention are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

In drawings which depict non-limiting embodiments of the invention:

FIG. 1 is a Gantt-type temporal plot showing the procedures involved ina typical prior art radiation treatment technique;

FIG. 2 is a Gantt-type temporal plot showing the timing of theprocedures involved in a method for radiation treatment according to aparticular embodiment of the invention;

FIG. 3 is a schematic plan view of a multi-leaf collimator suitable foruse in implementing the method of FIG. 2;

FIG. 4 is a schematic depiction of a radiation treatment system suitablefor implementing the method of FIG. 2 according to a particularembodiment of the invention;

FIG. 5 is a schematic description of the optimization and radiationdelivery procedures of the FIG. 2 method according to a particularembodiment of the invention;

FIGS. 6A, 6B and 6C (collectively, FIG. 6) schematically depict theassumptions which may used to implement relatively rapid fractionaloptimization in comparison to the initial optimization of the FIG. 2method;

FIG. 7 is a Gantt-type temporal plot showing the timing of theprocedures involved in a method for radiation treatment according toanother embodiment of the invention; and

FIG. 8 is a schematic description of the imaging, optimization andradiation delivery procedures of the FIG. 7 method according to aparticular embodiment of the invention.

DETAILED DESCRIPTION

Throughout the following description, specific details are set forth inorder to provide a more thorough understanding of the invention.However, the invention may be practiced without these particulars. Inother instances, well known elements have not been shown or described indetail to avoid unnecessarily obscuring the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative, ratherthan a restrictive, sense.

Aspects of the invention provide methods for radiation treatment of asubject involving one or more fractional treatments. In accordance withparticular embodiments, a fractional treatment comprises: obtainingfractional image data pertaining to a region of interest of the subject;performing a fractional optimization of a radiation treatment plan todetermine optimized values of one or more radiation delivery variablesbased at least in part on the fractional image data; and delivering afraction of the radiation treatment plan to the region of interest usingthe optimized values of the one or more radiation delivery variables asone or more corresponding parameters of the radiation treatment plan. Aportion of performing the fractional optimization may overlap temporallywith a portion of either or both of: obtaining the fractional image dataand delivering the fraction of the radiation treatment plan. The methodsmay involve performing an initial optimization based on initial imagedata. The initial optimization may use a different optimizationtechnique than the fractional optimization.

FIG. 2 is a temporal chart which schematically illustrates the timing ofthe procedures involved in radiation treatment method 100 according to aparticular embodiment of the invention. As illustrated in FIG. 2,radiation treatment method 100 may generally be divided into a planinitialization process 102 and a fractional process 104. Planinitialization process 102 is performed once per subject to beirradiated. Fractional process 104 is performed once for each fraction(i.e. fractional process 104 may be performed a plurality of times tocomplete a radiation treatment).

Plan initialization process 102 of radiation treatment method 100 startsin block 110 which involves obtaining an initial image of a region ofinterest of the subject. Typically, although not necessarily, a subjectwill visit a treatment facility so that the block 110 initial image maybe obtained from the subject. The region of interest imaged in block 110may include the target volume and the surrounding tissue. The block 110procedure for obtaining the initial image may be substantially similarto prior art imaging procedures and may be accomplished using anysuitable imaging equipment and procedures. Preferably, the block 110initial image is obtained using a three-dimensional imaging technique.By way of non-limiting example, the block 110 initial image may beobtained using conventional CT scanning, cone-beam CT scanning, magneticresonance imaging (MRI), positron emission tomography (PET), ultrasoundimaging, tomosynthesis or the like.

Once the block 110 initial image is obtained, the radiation treatmentplan is initialized in block 120. The subject need not be present at thetreatment facility for the block 120 treatment plan initialization. Theblock 120 treatment plan initialization may be accomplished usingprocedures substantially similar to prior art techniques forinitializing radiation treatment plans. In the illustrated embodiment,the block 120 treatment plan initialization comprises determining a setof treatment plan objectives and initializing the parameters of thetreatment plan. The parameters of a treatment plan may comprise a numberof fixed parameters and a number of variable parameters. The block 120treatment plan initialization may be based on information obtained fromthe block 110 initial image. The objectives of a radiation treatmentplan may be prescribed by medical professionals and may specify desireddose levels (or a range of desired dose levels) to be delivered to thetarget volume and maximum desired dose levels to be delivered tosurrounding tissue and organs.

A non-limiting example of a set of radiation treatment plan objectivesis shown in Table 1. The Table 1 treatment plan objectives are derivedfrom the RTOG Prostate IMRT Protocol for providing radiation treatmentto a cancerous target volume located in the subject's prostate.

TABLE 1 Treatment Plan Objectives Non-Target No more than 15% No morethan 25% No more than 35% No more than 50% Organ vol. receives dose vol.receives dose vol. receives dose vol. receives dose Objectives thatexceeds that exceeds that exceeds that exceeds Bladder 80 Gy 75 Gy 70 Gy65 Gy Rectum 75 Gy 70 Gy 65 Gy 60 Gy Minimum Target Volume Dose MaximumTarget Target Objectives (over more than 98% of target vol.) Volume DosePlanning Treatment Volume 73.8 Gy 79 Gy (Target Volume)

The Table 1 treatment plan objectives represent one particular set oftreatment plan objectives for one particular treatment. It will beappreciated by those skilled in the art that treatment plan objectivesmay generally differ from those of Table 1. In some embodiments, atreatment plan will specify a maximum dose to be delivered to a “shell”.A shell typically surrounds the target volume, but may not contain anyimportant healthy organs. The dose delivery maximum for a shell may beincluded in the treatment plan objectives to eliminate “hot spots” whichmay be outside of the target volume and which may not part of theNon-Target Organ Objective specified by the plan objectives.

Treatment plan objectives may optionally involve truncation of thevolume of the non-target organs or some other procedure for removingportions of the volume of the non-target organs from consideration. Forexample, when treating the prostate, portion(s) of the bladder and/orportion(s) of the rectum may be located sufficiently far from the targetvolume such that these portion(s) would receive negligible dose. In suchcases, it may be desirable to remove these portion(s) from considerationin the treatment plan. The removal of volume from non-target organs maymake it more difficult to achieve the treatment plan objectives, as themaximum dose limits for the non-target organs represent a percentage ofa smaller volume.

Initializing the treatment plan parameters as part of the block 120initialization may depend on the available radiation treatment equipment(not explicitly shown in FIG. 2) and the types of radiation deliveryplans suitable for use with such radiation treatment equipment. In someembodiments of the invention, the radiation treatment plan used inmethod 100 comprises a plan suitable for use with direct apertureoptimization (DAO) radiation treatment.

DAO radiation treatment typically involves movement of a radiationsource to a number of discrete locations (e.g. around a subject) andthen directing one or more beams at the subject from each such discretelocation. Each individual location of the radiation source relative tothe subject results in a different beam orientation. The orientations ofthe beams relative to the subject and the number of beams directedtoward the subject in each orientation may be referred to as the “beamarrangement” of the treatment plan. The beam arrangement characteristicsrepresent parameters of a DAO radiation treatment plan. The block 120treatment plan initialization may involve determining thecharacteristics of the beam arrangement (i.e. the orientations of thebeams relative to the subject and the number of beams directed towardthe subject in each orientation).

In DAO systems, the cross-sectional shape of each beam directed towardthe patient may be controlled by a multi-leaf collimator (MLC) or someother suitable beam-shaping device. A typical MLC 33 is shownschematically in FIG. 3 and comprises a plurality of opposing pairs ofcollimator leaves 36. Collimator leaves 36 (which may be fabricated frommaterial that is at least partially impermeable to radiation) areindividually movable in the directions of double-headed arrow 41 tocontrol the shape of one or more openings(s) 38 and to thereby controlthe cross-section of the beam. As shown in dashed lines, MLC 33 may alsobe pivotable about axis 37, which, in the FIG. 3 illustration, extendsinto and out of the page. Pivotal motion about axis 37 permits furtheradjustment of the cross-section of the beam. Because MLC 33 controlsaperture 38 which in turn determines the cross-section of the individualbeams in DAO systems, the individual beams in a DAO radiation treatmentsystem are often referred to in the art as “apertures”. In addition tocontrolling the cross-section of each beam, DAO treatment systemstypically control the quantity or “weight” of the radiation beam thatpasses through MLC 33 and impinges on the subject.

The beam apertures (as controlled by the MLC leaf positions and,optionally, the MLC orientation) and the beam weights represent otherDAO treatment plan parameters which may be initialized in block 120. Insome embodiments, the MLC leaf positions and orientations areinitialized in block 120 such that the shapes of the resultant beamsmatch a projection of the target volume (e.g. to approximate a beam'seye view outline of the target volume) and the beam weights areinitialized in block 120 to have equal values which may be set so thatthe mean dose in the target volume will equal a prescribed doseobjective.

After initializing a plan in block 120, method 100 proceeds to block 130which involves optimizing the one or more of the treatment planparameters in effort to achieve the plan objectives. The subject neednot be present at the treatment facility for the block 130 initialoptimization. In some embodiments, the block 130 initial optimizationmay be performed in accordance with procedures substantially similar toprior art techniques for optimizing radiation treatment plan parametersin effort to meet treatment plan objectives. In other embodiments, theblock 130 initial optimization may differ from prior art optimizationtechniques. Optimizing treatment plan parameters in effort to meet theplan objectives typically involves adjusting various treatment planparameters in an attempt to minimize (at least to an acceptable level) acost function (also referred to as an objective function).

Typically, a cost function is constructed on the basis of the treatmentplan objectives and may provide a metric of plan quality based on how agiven plan is expected to meet the plan objectives. A typical costfunction combines an expression that reflects the target volume and anexpression that reflects the surrounding tissue. The cost function mayincrease when the radiation delivered to the target volume is below acertain minimum target threshold and/or when the radiation delivered tothe target volume is above a certain maximum target threshold and maydecrease when the radiation delivered to the target volume is betweenthe minimum and maximum target thresholds. The cost function mayincrease when the radiation delivered to certain regions of thesurrounding tissue (e.g. tissue corresponding to important non-targetorgans) is above a minimum non-target threshold. Various aspects of thecost function may be weighted differently than others.

In one non-limiting example, a quadratic cost function is provided whichincludes a set of terms for the target volume and one set of terms forthe critical non-target structures (e.g. non-target organs). For thetarget, the minimum and the maximum allowed dose (D_(min) and D_(max))are specified together with the respective weights (w_(t) ^(min) andw_(t) ^(max)) and the target terms of the cost function are given by:

$\begin{matrix}{F_{t} = {{\frac{w_{t}^{\min}}{N_{t}}{\sum\limits_{i = 1}^{N_{t}}{( {D_{i} - D_{\min}} )^{2}{H( {D_{\min} - D_{i}} )}}}} + {\frac{w_{t}^{\max}}{N_{t}}{\sum\limits_{i = 1}^{N_{t}}{( {D_{i} - D_{\max}} )^{2}{H( {D_{i} - D_{\max}} )}}}}}} & (1)\end{matrix}$where H(x) is a step function given by:

$\begin{matrix}{{H(x)} = \begin{Bmatrix}1 & {x \geq 0} \\0 & {x < 0}\end{Bmatrix}} & (2)\end{matrix}$

For each critical non-target structure (e.g. non-target organ), thevolume receiving a dose greater than D₁ should be less than V₁. Onetechnique for implementing this condition is described by Bortfeld etal. (Clinically relevant intensity modulation optimization usingphysical criteria. In Proceedings of the XII International Conference onthe Use of Computers in Radiation Therapy, Salt Lake City, Utah,1997:1-4.) and involves defining another dose D₂ such that the volumethat receives the dose D₂ is V₁. The critical structure dose volume termof the cost function is then given by:

$\begin{matrix}{F_{OAR} = {\frac{w_{OAR}}{N_{OAR}}{\sum\limits_{i = 1}^{N}{( {D_{i} - D_{1}} )^{2} \cdot {H( {D_{i} - D_{1}} )} \cdot {H( {D_{2} - D_{i}} )}}}}} & (3)\end{matrix}$Equation (3) ensures that only voxels receiving dose between D₁ and D₂are penalized in the cost function. For each critical structure, anunlimited number of dose-volume conditions can be specified.

Block 130 involves varying treatment plan parameters in effort tominimize the cost function. The particular treatment plan parametersthat are varied during optimization are referred to herein as “radiationdelivery variables”. As discussed above, in a DAO radiation treatmentsystem, the radiation treatment plan parameters include, withoutlimitation: the characteristics of the beam arrangement (e.g. theorientations of the beams and the number of beams directed toward thesubject in each orientation); the positions of the MLC leaves 36 foreach beam; the orientation of MLC 33 about axis 37 for each beam; andthe weight of each beam. In particular embodiments, treatment planparameters used as radiation delivery variables during the block 130initial optimization are limited to: the positions of the MLC leaves 36for each beam and the weight of each beam. This limitation is notnecessary. Optionally, DAO optimizations (including the block 130initial optimization and the block 150 fractional optimization discussedin more detail below) may involve variation of other treatment planparameters, such as the pivotal orientation of MLC 33 about axis 37,various characteristics of the beam arrangement or the like. Theremainder of this description assumes, unless otherwise stated, that theradiation delivery variables include only the positions of the MLCleaves 36 for each beam and the weight of each beam. This assumption ismade without loss of generality and is made for the purpose ofsimplifying explanation only.

In particular radiation treatment plans, the radiation deliveryvariables take on different values at different control points. Eachradiation treatment plan may comprise a number of control points.Control points may (but need not necessarily) correspond to fixedparameters of a radiation treatment plan. For example, in someembodiments, the control points of a DAO radiation treatment plancorrespond to the individual beams of the beam arrangement. In suchembodiments, the radiation delivery variables (e.g. the positions of theMLC leaves 36 and the beam weight) may be different for each of theindividual beams of the beam arrangement.

The block 130 optimization process involves optimizing the radiationdelivery variables in effort to minimize the cost function. In oneparticular embodiment, the block 130 optimization involves iteratively:selecting and modifying one or more radiation delivery variable(s);evaluating the quality of the dose distribution resulting from themodified optimization variable(s)—e.g. by computing the cost function;and then making a decision to accept or reject the modified radiationdelivery variable(s).

Typically, although not necessarily, the block 130 optimization will besubject to a number of constraints. In some embodiments, suchconstraints may reflect various physical limitations of the radiationtreatment system (e.g. a range of acceptable positions for MLC leaves 36and/or a range of acceptable beam intensities). In some embodiments,these optimization constraints may be determined by image informationobtained in block 110. For example, it may be desirable to constrain therange of the MLC leaves 36 such that the cross-sectional shape of eachbeam does not exceed the beam's eye view projection of the targetvolume. In some embodiments, the block 130 constraints are related tothe amount of change in one or more radiation delivery variables thatmay be permitted between successive optimization iterations (e.g. amaximum change of MLC leaf position between successive optimizationiterations).

It will be appreciated by those skilled in the art, that the block 130optimization may generally be accomplished using any suitableoptimization technique. Non-limiting examples of suitable optimizationtechniques include: Nelder-Mead method optimization (the Amoeba method),gradient method optimization, subgradient method optimization, simplexmethod optimization, ellipsoid method optimization, simulated annealingoptimization, quantum annealing optimization, stochastic tunnelingoptimization, genetic optimization algorithms or the like. The block 130optimization may also involve variations and combinations of theseoptimization techniques.

The conclusion of the block 130 initial optimization marks the end ofplan initialization process 102. At the conclusion of planinitialization process 102, method 100 has access to an initialoptimized radiation treatment plan. The initial optimized radiation planincludes a set of initial radiation delivery variables which isoptimized for delivery of radiation to the subject based on the initialimage obtained in block 110.

Method 100 then enters its first fractional process 104. As mentionedabove, fractional process 104 may be implemented once for each fractionof radiation treatment method 100. It is generally preferable (althoughnot necessary) for the subject to remain present at the treatmentfacility for each iteration of fractional process 104. In someembodiments, the subject can remain on the treatment “couch” for theduration of each fractional process 104.

Fractional process 104 commences in block 140 which involves obtainingan updated image of the region of interest. This block 140 updated imagemay be referred to as a “fractional image”. Like the block 110 initialimage, the region of interest for the block 140 fractional image mayinclude the target volume and the surrounding tissue. In general, theblock 140 fractional image may be obtained using any suitable imagingtechnique, including any of the imaging techniques discussed herein forblock 110. However, the block 140 fractional image need not be obtainedusing the same imaging technique as the block 110 initial image. Inparticular embodiments, the block 140 fractional image is obtainedaccording to a tomosynthesis imaging technique which has a relativelyshort image acquisition time and a relatively short image reconstructiontime.

The block 140 fractional image is obtained at a time proximate to thedelivery of a fractional radiation dose (when compared to the block 110initial image. Also, the subject may remain in one general positionbetween the block 140 fractional image and the block 160 fractionalradiation delivery discussed further below. Consequently, the block 140fractional image represents a more accurate (e.g. more current)representation of the region of interest than the block 110 initialimage. By way of non-limiting example, the block 140 fractional imagemay account for changes in shape or size of the target volume, movementof the target volume, changes in shape or size of neighboringtissue/organs or the like which may have occurred between the time ofthe block 110 initial image and the block 140 fractional image.

In the FIG. 2 embodiment, once a fractional image is obtained in block140, method 100 proceeds to block 150 which involves further optimizingthe radiation delivery variables to account for new information obtainedfrom the block 140 fractional image. In the first iteration offractional process 104, the block 150 fractional optimization mayinvolve further optimizing radiation treatment plan of planinitialization process 102 (i.e. the output of block 130). That is, thefirst iteration of the block 150 fractional optimization may involveinitializing the treatment plan parameters with the parameters of theblock 130 initial optimized radiation treatment plan and then furtheroptimizing the radiation delivery variables to account for the newinformation obtained in the block 140 fractional image. In subsequentiterations of fractional process 104, the block 150 fractionaloptimization may involve further optimizing the radiation treatment planof plan initialization process 102 or the block 150 fractionaloptimization may involve further optimizing the radiation treatment planof the previous block 150 optimization.

The output of block 150 is a further optimized radiation treatment plan(including a further optimized set of radiation delivery variables) thatincorporates the changes in the subject's region of interest which mayhave occurred between the block 110 initial image and the block 140fractional image processes. Since the block 150 fractional optimizationaccounts for these potential changes to the subject's region ofinterest, the resultant further optimized radiation treatment plan helpsto achieve the general desire of imparting a prescribed radiation doseto the target volume, while minimizing (to the extent possible) the doseimparted to surrounding healthy tissue and organs.

The block 150 fractional optimization may differ from the block 130initial optimization. Preferably, the block 150 fractional optimizationtakes less time than the block 130 initial optimization. In particularembodiments, the block 150 fractional optimization takes less than 10minutes. In preferred embodiments, the block 150 fractional optimizationprocess takes less than 5 minutes. The relatively short fractionaloptimization process of block 150 helps to achieve the desire ofreducing the amount of time required for each fraction.

In particular embodiments, it is assumed that the changes in thesubject's region of interest between the block 110 initial image and theblock 140 fractional image processes are relatively minor. Thisassumption leads to the corresponding assumption that the block 150fractional optimization should obtain a result (i.e. a further optimizedset of radiation delivery variables) that is relatively close to itsinitial set of radiation delivery variables. As discussed above, theinitial set of radiation delivery variables for the block 150 fractionaloptimization may include those of the radiation treatment plandetermined in plan initialization process 102 or those of the previousiteration of block 150. As discussed in more detail below, theseassumptions permit the use of several time-saving procedures for theblock 150 fractional optimization which would not be suitable orpossible for use with the block 130 initial optimization.

Fractional process 104 also involves delivering radiation in block 160.The block 160 fractional radiation delivery comprises delivering aparticular fraction of the radiation treatment plan in accordance withthe further optimized set of radiation delivery variables determined inthe block 150 fractional optimization. As shown in FIG. 2, the block 160fractional radiation delivery procedure may commence prior to completionof the block 150 fractional optimization—i.e. a portion of the block 150fractional optimization and a portion of the block 160 fractionalradiation delivery may occur simultaneously. The ability to commence theblock 160 radiation delivery prior to completion of the block 150fractional optimization may also be based on the assumption that thechanges in the subject's region of interest between the block 110initial image and the block 140 fractional image processes arerelatively minor.

In one embodiment, the block 150 fractional optimization procedurecomprises cycling through all of the individual beams (i.e. apertures)in the beam arrangement and optimizing the radiation delivery variablesof each beam (e.g. the MLC leaf positions and beam weight) as it cyclesthrough the beams. However, instead of continually cycling through allof the beams until the radiation delivery variables are completelyoptimized (at least to a clinically acceptable level), the block 150optimization may be performed for a period T₁. The period T₁ maycomprise a threshold number of optimization iterations, a thresholdtime, achievement of a threshold level for the cost function,achievement of a threshold rate of change of the cost function betweeniterations or the like.

After the period T₁, the radiation delivery variables of a first beammay be fixed. The first beam of the block 160 radiation delivery may bepermitted to commence as soon as the radiation delivery variables of thefirst beam are fixed (i.e. after the period T₁). Once the radiationdelivery variables of the first beam are fixed, the first beam isremoved from the block 150 fractional optimization and the block 150fractional optimization continues to optimize the radiation deliveryvariables of the remaining beams while radiation is being delivered inthe first beam. After continuing to optimize the remaining beams for asecond period T₂, the radiation delivery variables of a second beam arefixed, whereupon the second beam of the block 160 radiation delivery maybe permitted to commence and the block 150 optimization can remove thesecond beam from the optimization process and continue optimizing forthe remaining available beams. This procedure can be repeated until theblock 150 fractional optimization is completed with the final beam. Asdiscussed in more detail below, the optimization of particular beams andthe random variables for each such beam may (but need not necessarily)proceed in a particular order to facilitate the overlap of the block 160radiation delivery and the block 150 fractional optimization.

This procedure for commencing the block 160 fractional radiationdelivery prior to the completion of the block 150 fractionaloptimization is schematically depicted in method 170 of FIG. 5. Method170 commences in block 172 which involves optimizing the radiationdelivery variables for all of the beams in the beam arrangement of theradiation delivery plan. In the illustrated embodiment, it is assumedthat the total number of beams in the beam arrangement is n. Block 174involves evaluating whether the period T₁ has expired. If the period T₁has not expired (block 174 NO output), then method 170 returns to block172 and continues optimizing the radiation delivery variables for all nbeams.

If, on the other hand, the period T₁ has expired (block 174 YES output),then method 170 permits delivery of the first beam of radiation in block178. Simultaneously, method 170 proceeds to block 176, where the firstbeam is removed from the block 150 optimization process and the block150 optimization process continues optimizing the remaining n−1 beams.When T₂ has expired (block 180 YES output), method 170 permits deliveryof the second beam of radiation in block 184 and simultaneously proceedsto block 182, where the second beam is removed from the block 150optimization process and the block 150 optimization process continuesoptimizing the remaining n−2 beams. This process may continue untilblock 186, which involves optimizing the radiation delivery variablesfor the last (n^(th)) beam. When the last period (T_(n)) expires (block188 YES output), method 170 permits delivery of the last (n^(th)) beamin block 190 and is completed.

While the periods T₁, T₂, . . . may be the same for each iteration, thisis not generally necessary. Preferably, to achieve a high efficiency,the temporal duration of the periods T₁, T₂, . . . is less than the timerequired to deliver the radiation for a particular beam. That is,preferably the block 150 fractional optimization for a particular beamtakes less time than the block 160 delivery of radiation for thepreceding beam. With this high efficiency condition, beams will be ableto be delivered as soon as they are permitted to be delivered and therewill be no “dead time” between the block 160 delivery radiation forsuccessive beams. Again, however, this high efficiency condition is notnecessary, as there will still be efficiency gains for any overlap ofthe block 150 fractional optimization and the block 160 radiationdelivery.

The first iteration of fractional process 104 concludes at the end ofthe block 160 radiation delivery. Fractional process 104 may be repeatedas many times as is desirable to achieve the radiation treatment plan.In some embodiments, each fraction is designed to deliver acorresponding fractional amount of the desired dose as set out in theradiation treatment plan objectives. That is, if there are ten fractionsin the treatment plan, then each fraction is configured to deliver 1/10of the prescribed dose. In other embodiments, the radiation treatmentplan may be updated after each fractional delivery to account moreprecisely for the radiation actually delivered during a particularfraction. This treatment plan updating is not explicitly shown in method100 of FIG. 2. However, in some embodiments, such treatment planupdating could occur between each iteration of fractional process 104 sothat it could be done without requiring the patient to be present at thetreatment facility. In other embodiments, this treatment plan updatingcould be done after each fractional imaging procedure 140.

FIG. 4 depicts a radiation treatment system 200 according to aparticular embodiment of the invention which may be suitable forperforming radiation treatment method 100. Radiation treatment system200 comprises a radiation source 212 capable of generating or otherwiseemitting a beam 214 of radiation for treatment of subject S. Radiationsource 212 may comprise a linear accelerator, for example. As discussedabove, radiation treatment system 200 may comprise a beam-shaping device33 for controlling the shape of beam 214. Beam-shaping device 33 maycomprise a multi-leaf collimator, for example.

During fractional process 104 of method 100, subject S may be positionedon a table or “couch” 215 which can be placed in the path of beam 214.System 200 comprises one or more actuators 234 and movable parts 216that permit the location of radiation source 212 and orientation ofradiation beam 214 to be moved relative to subject S. Actuators 234 andmovable parts 216 may be referred to collectively as a beam positioningmechanism 213. Beam positioning mechanism 213 together with radiationsource 212 may be referred to as a radiation delivery system 230.Radiation delivery system 230 provides the radiation used to treatsubject S.

Beam positioning system 213 may function to provide the various beamorientations of a DAO radiation delivery plan. In the illustrated system200, movable parts 216 of beam positioning mechanism 213 comprises agantry 217 which supports radiation source 212 and which can be rotatedabout an axis 218. Axis 218 and beam 214 intersect at an isocenter 220.Beam positioning mechanism 213 may also comprise a movable couch 215. Inexemplary system 200, couch 215 can be translated in any of threeorthogonal directions (shown in FIG. 3 as X, Y, and Z directions) andcan be rotated about an axis 222. In some embodiments, couch 215 can berotated about one or more of its other axes. The location of source 212and the orientation of beam 214 can be changed (relative to subject S)by moving one or more of movable parts 216 of beam positioning mechanism213.

In the illustrated embodiment, radiation treatment system 200 comprisesan imaging system 232. Imaging system 232 may be used for the block 140fractional imaging process and, optionally, for the block 110 initialimaging process. In the illustrated embodiment, imaging system 232comprises a cone-beam CT imaging apparatus. As discussed above, avariety of other imaging apparatus (e.g. conventional CT scanning,cone-beam CT scanning, magnetic resonance imaging (MRI), positronemission tomography (PET), ultrasound imaging, tomosynthesis or thelike) may be suitable for implementing radiation delivery method 100 andradiation treatment system 200 may generally incorporate any suchimaging apparatus. Exemplary cone-beam CT imaging system 232 comprisesan X-ray source 244 capable of generating or otherwise emitting animaging X-ray beam 242. X-ray source 244 may comprise one or morebeam-shaping devices (not explicitly shown) for controlling the shape ofimaging beam 242.

Subject S may also be positioned on couch 215 during the block 140fractional imaging process and, optionally, for the block 110 initialimaging process. Couch 215 may be placed in the path of imaging beam242. The cone-beam CT imaging system 232 of the illustrated embodimentalso comprises a detector unit 238 located on the opposing side of couch215 from X-ray source 244. Detector unit 238 comprises one or moresensors that are sensitive to imaging beam 242. Imaging system 200 maycomprise one or more actuators 246 and movable parts 247 that permit thelocation of X-ray source 244, the orientation of imaging beam 242 andthe location of detector unit 238 to be moved relative to subject S.

In exemplary cone-beam CT imaging system 232, movable parts 247comprises a gantry 248 which supports X-ray source 244 and detector unit238 on opposing sides of couch 215. In the illustrated embodiment,gantry 248 of imaging system 232 is rotatable about axis 218 (i.e. thesame axis about which gantry 217 of radiation delivery system 230 iscapable of rotating). However, this is not necessary. In general,movable parts 247 may rotate X-ray source 244 and detector unit 238about a different axis.

In the illustrated embodiment, axis 218 and imaging beam 242 intersectat an isocenter 236. It may be desirable that isocenter 236 of imagingsystem 232 be located at a particular location within subject S for theblock 140 fractional imaging process. For example, it may be desirablethat isocenter 236 be located within (or in close proximity) to thetarget volume in subject S. In particular embodiments, it may bedesirable for isocenter 220 of radiation delivery system 230 bepositionable at the same location (or at least within a thresholdvicinity of the same location) during the block 160 fractional radiationdelivery. In the illustrated embodiment of system 200, this commonisocenter location may be implemented by moving couch 215 in the xdirection between the block 140 fractional imaging process and the block160 fractional radiation delivery, for example. In other embodiments,system 200 may be constructed such that the isocenters 220, 236 ofradiation delivery system 230 and imaging system 232 are alwayscoincident (or within a threshold proximity to one another). Forexample, X-ray source 244, detector unit 238 and radiation source 212may be mounted on a single rotational gantry system. In one particularembodiment, X-ray source 244, detector unit 238 and radiation source 212are mounted on a single rotational gantry such that imaging beam 242 isorthogonal to treatment radiation beam 214.

Radiation treatment system 200 comprises a control system 223. Controlsystem 223 may be configured to control: the relative positions of thecomponents of beam positioning mechanism 213; various othercharacteristics of radiation delivery system 230 (e.g. the intensityoutput of radiation source 212 and the characteristics of beam-shapingdevice 33); and the operation (including movement and image processing)of imaging system 232.

In the illustrated embodiment, control system 223 is schematicallyillustrated as a single unit. This is not necessary. Control system 223may be distributed. For example, control system 223 may compriseseparate control subsystems for controlling beam positioning mechanism213, radiation delivery system 230 and/or imaging system 232. Controlsystem 223 may generally comprise hardware components and/or softwarecomponents. Control system 223 may comprise one or more data processors,together with suitable hardware, including, by way of non-limitingexample: accessible memory, logic circuitry, drivers, amplifiers, A/Dand D/A converters and like. Such data processors may comprise, withoutlimitation, a microprocessor, a computer-on-a-chip, the CPU of acomputer or any other suitable microcontroller. Control system 223 maycomprise a plurality of data processors.

Control system 223 may be programmed with software or may otherwise haveaccess to software (e.g. a program product or the like) which, whenexecuted, may cause control system 223 to implement method 100 discussedabove and method 300 discussed below.

As mentioned briefly above, in particular embodiments it may assumedthat the changes in the subject's region of interest between the block110 initial image and the block 140 fractional image processes arerelatively minor which leads to the corresponding assumption that theblock 150 fractional optimization should obtain a result (i.e. a furtheroptimized set of radiation delivery variables) that is relatively closeto its initial set of radiation delivery variables. These assumptionsare schematically illustrated in FIG. 6.

FIG. 6A schematically depicts a cost function 192 as a function of theradiation delivery variables. The set of radiation delivery variables194 represents a particular set of radiation delivery variables forwhich cost function 192 is minimized. The set of radiation deliveryvariables 194 may represent the initial conditions for the block 150fractional optimization. As discussed above, initial radiation deliveryvariables 194 for the block 150 fractional optimization may includethose of the radiation treatment plan determined in plan initializationprocess 102 (i.e. block 130) or those of the previous iteration of block150. In any event, initial radiation delivery variables 194 shown inFIG. 6A are based on old image data—i.e. the block 110 image data in thecase of initial radiation delivery variables 194 determined in block 130or a previous iteration of block 140 image data in the case of initialradiation delivery variables determined in a previous iteration of block150.

The acquisition of new image data in block 140 of the current iterationof fractional process 104 causes a shift in the cost function from 192to 192′ as shown by arrow 196 in FIG. 6B. The shift in cost functionfrom 192 to 192′ is associated with changes (e.g. movement, deformationor the like) of the target volume and/or non-target tissue which mayhave occurred between acquisition of the previous image data (on whichinitial radiation delivery variables are based) and acquisition of thecurrent image data in block 140 of the current iteration of fractionprocess 104. The block 150 fractional optimization involves finding anew set of radiation delivery variables 194′ which correspond to aminimum of shifted cost function 192′. The block 150 fractionaloptimization is schematically depicted as arrow 150 in FIG. 6C.

The assumptions that the changes in the subject's region of interestbetween successive imaging procedures are relatively minor and that theblock 150 fractional optimization should obtain a further optimized setof radiation delivery variables 194′ that is relatively close to itsinitial set of radiation delivery variables 194 may correspond to themathematical situation that shifted cost function 192′ exhibits no localminima between the initial set of radiation delivery variables 194 andthe new set of radiation delivery variables 194′. The assumption thatthere are no local minima between the initial set of radiation deliveryvariables 194 and the new set of radiation delivery variables 194′permit the use of several time-saving procedures for the block 150fractional optimization which would not be suitable or possible for usewith the block 130 initial optimization.

In one embodiment, the block 150 fractional optimization makes use of adifferent mathematical optimization technique than the optimizationtechnique used in the block 130 optimization. Some optimizationtechniques, such as the gradient method and Newton's method for example,represent relatively “rapid” optimization techniques (e.g. rapid interms of number of iterations and/or some other measure of computationalresources), but are relatively susceptible to the presence of localminima between the initial conditions and the desired solution. Suchoptimization techniques would typically be unsuitable for use in theblock 130 optimization because the block 130 optimization is preferablyable to overcome local minima. However, such optimization techniquescould be suitable for the block 150 fractional optimization.Accordingly, the block 150 fractional optimization may involve the useof mathematical optimization techniques that are relatively rapidcompared to the optimization technique employed in the block 130 initialoptimization. Similarly, the block 130 initial optimization may involvethe use of mathematical optimization techniques that are relatively morecapable of overcoming local minima than the optimization techniqueemployed in the block 150 fractional optimization.

Other embodiments involve reducing the size of the search space in theblock 150 fractional optimization relative to the size of the searchspace in the block 130 initial optimization in order to make the block150 fractional optimization rapid in relation to the block 130 initialoptimization.

In one particular embodiment, reducing the search space of the block 150fractional optimization involves the use of constraints for the maximumchanges of one or more radiation delivery variables between successiveiterations of the optimization process. The block 150 fractionaloptimization may involve using more stringent constraints for themaximum changes of one or more radiation delivery variables betweensuccessive iterations of the optimization process when compared to theblock 130 initial optimization. For example, where the radiationdelivery variables include the MLC leaf positions for each beam, theblock 150 fractional optimization may assign maxima (or more stringentmaxima) to the changes in the MLC leaf positions between successiveiterations of the optimization process.

In one particular embodiment, the block 130 optimization may involveconstraints for the maximum changes of one or more radiation deliveryvariables between successive iterations wherein the inter-iterationconstraints on the change(s) to the radiation delivery variable(s) startat an initial maximum and then decrease according to a particularschedule function as the optimization proceeds. In this embodiment, theblock 150 inter-iteration constraints on the change(s) to the radiationdelivery variable(s) may start at an initial maximum that is less thanthe initial maximum of the block 130 optimization and may then decreaseaccording to a similar schedule function or according to a differentschedule function as the optimization proceeds.

In another embodiment, reducing the search space of the block 150fractional optimization involves the use of constraints on the maximumaggregate change(s) in one or more of the radiation delivery variablesduring the block 150 optimization process. The block 150 fractionaloptimization may involve using more stringent constraints on the maximumaggregate change(s) in one or more of the radiation delivery variablescompared to the block 130 initial optimization process. For example,where the radiation delivery variables include the MLC leaf positionsfor each beam, the block 150 fractional optimization process may assignmaxima (or more stringent maxima) to the change of the MLC leafpositions between their initial values and their final (optimized)values.

In one particular embodiment, the block 130 optimization involves theuse of constraints on radiation delivery variables that reflect physicallimitations. For example, it may not be possible to open a MLC leafbeyond a certain position and it may not be possible to provide negativebeam weights. Accordingly, such limitations may impose constraints. Inaddition, some values of the radiation delivery variables are clearlyundesirable (e.g. allowing the MLC leaves to open beyond the projectionof the beam' eye view of the target volume) and are imposed asconstraints in the block 130 process. In such embodiments, the block 150fractional optimization may comprise adding new constraints to thoseused in the block 130 initial optimization. Such new constraints mayrelate to the radiation delivery variables between their initial valuesand their final (optimized) values.

In still another embodiment, reducing the search space of the block 150fractional optimization involves reducing (relative to the block 130optimization) the randomness of selecting radiation delivery variable(s)for variation in each iteration of the optimization process and/orreducing the randomness of the amount/direction by which the selectedradiation delivery variable is varied in each iteration of theoptimization process. For example, in some embodiments, each iterationof the block 130 initial optimization involves randomly selecting one ormore of: the particular beam in which to vary a radiation deliveryvariable; the particular radiation delivery variable (e.g. theparticular MLC leaf or beam weight) to vary; the direction in which tovary the particular radiation delivery variable to vary; and the amount(amplitude) of variation to apply to the particular radiation variableto vary. In such embodiments, the block 150 fractional optimization maydetermine the particular beam in which to vary a particular radiationdelivery variable for a particular iteration of the optimization processby cycling through each beam in order. The block 150 fractionaloptimization may also determine, for each beam, the particular radiationdelivery variable to vary in a particular iteration of the optimizationprocess by cycling through the MLC leaf positions for the beam and thebeam weight in a particular order.

Once the particular radiation delivery variable to vary is decided, then(instead of applying a random change) to the variable, the direction ofthe change and/or the amount (amplitude) of the change in a particulariteration may be based on the success of one or more previousiteration(s). For example, if it was determined in a previous iterationthat moving a particular MLC leaf inwardly cause a correspondingdecrease in the cost function, then an adjacent MLC leaf varied in thecurrent iteration may also be moved inwardly. As another example, if itwas determined over a number of previous iterations that the rate ofdecrease of the cost function for a given movement of a particular MLCleaf was decreasing, then an amount of the current movement of that MLCleaf and/or an adjacent MLC leaf could be reduced according to somefunction.

In still another embodiment, reducing the search space of the block 150fractional optimization involves changing the criteria (relative to theblock 130 criteria) for whether or not a variation of a radiationdelivery variable in a particular iteration of the optimization isaccepted. In the block 130 initial optimization it is typicallydesirable to permit some variations of radiation delivery variables inparticular iterations of the optimization which actually cause the costfunction to increase. This allows the block 130 optimization to escapefrom local minima in the cost function. For example, in someembodiments, variations of radiation delivery variables which increasethe cost function may be permitted with a probability given by theMetropolis condition. In contrast with this aspect of the block 130initial optimization, in particular embodiments of the block 150fractional optimization, the variation(s) of radiation deliveryvariable(s) in a particular iteration may be accepted only when theycorrespond to decreases in the cost function.

FIG. 7 is a Gantt-type temporal plot showing the timing of theprocedures involved in a method 300 for radiation treatment according toanother embodiment of the invention. Method 300 is similar in manyrespects to method 100 (FIG. 2) and the reference numbers used todescribe the features of method 300 are similar to those used todescribe method 100, except that the reference numbers corresponding tothe features of method 300 have a leading numeral “3” whereas thereference numbers corresponding to features of method 100 have a leadingnumeral “1”. Method 300 comprises a plan initialization process 302 thatis performed once for each subject and a fractional process 304 that isperformed once for each fraction of method 300. Plan initializationprocess 302 may be substantially similar to plan initialization process102 described herein.

Fractional process 304 of method 300 differs from fractional process 104of method 100. More particularly, as shown in FIG. 7, portions of theblock 340 fractional imaging process 340, the block 350 fractionaloptimization process and the block 360 fractional radiation deliveryprocess occur simultaneously (i.e. overlap temporally). The temporallyoverlapping fractional optimization (block 350) and fractional radiationdelivery (block 360) may be similar to the temporally overlappingfractional optimization and radiation delivery of blocks 150, 160described above. However, in method 300, the block 350 fractionaloptimization commences prior to the completion of the block 340fractional imaging process.

In one particular embodiment, the block 340 fractional imaging processcomprises a tomosynthesis process which may be implemented, for example,by a cone-beam CT imaging apparatus similar to that of imaging system200 (FIG. 4) described above. In such embodiments, the 360° rotation ofthe imaging system (e.g. X-ray source 244 and detector unit 238) aboutthe subject may be divided into a plurality of angular portions P₁, P₂ .. . P_(m). While the angular portions P₁, P₂ . . . P_(m) may be equal toone another, this is not necessary.

In one particular embodiment, each of the angular portions P₁, P₂ . . .. P_(m) of the block 340 fractional imaging process corresponds to thevarious beam orientations of the DAO treatment plan beam arrangement.For example, if the beam arrangement of the DAO treatment plan involvesdelivering one or more beams every 40°, then each portion P₁, P₂ . . .P_(m) of the block 340 fractional imaging process may also be 40°. Inother embodiments, the first angular portion P₁ is relatively large incomparison to the other angular portions P₂, . . . P_(m). In oneembodiment, the angular portions decrease in size after the firstangular portion P₁. During each portion P₁, P₂ . . . P_(m) of the block340 fractional imaging process, the imaging system may obtain aplurality of two-dimensional image projections (e.g. X-ray imageprojections). By way of non-limiting example, the imaging system mayobtain a two-dimensional image projection approximately every 1°.

After two dimensional image projections are obtained over the firstportion P₁, tomosynthesis techniques may be used to reconstruct athree-dimensional image of the region of interest from these imageprojections. While this three-dimensional reconstructed image may not beof maximum quality at this stage (because of the missing projectionsfrom portions P₂, . . . P_(m)) there may still be enough information topermit the block 350 fractional optimization to commence using thethree-dimensional reconstructed image. The block 360 fractionalradiation delivery may be permitted to commence after partiallycompleting the block 350 fractional optimization as discussed herein forblocks 150, 160.

In some embodiments, the block 340 fractional image data obtained inportion P₁ (or any of the other portions P₂, . . . P_(m)) may becombined with the block 310 initial image data to provide a higherquality image prior image prior to commencing the block 350 fractionaloptimization. In embodiments where the angular size of imaging portionP₁ corresponds to the angular difference between the various beamorientations of the beam arrangement, the block 350 fractionaloptimization and the block 360 fractional radiation delivery may beperformed for all of the beams at a particular beam orientation aftercompletion of the first imaging portion P₁ of the block 340 fractionalimaging process, although this is not necessary.

After obtaining image data from portion P₁ (and possibly commencing theblock 350 fractional optimization and the block 360 fractional radiationdelivery), image data may be obtained from portion P₂. Image data may beobtained from portion P₂ in essentially the same method as image data isobtained from portion P₁. After obtaining image data in P₂, the imagedata from portions P₁ and P₂ may be combined using tomosynthesis methodsto generate a three-dimensional reconstructed image. Subsequent portionsof the block 350 optimization process may be based on the newreconstructed image which combines the image data from portions P₁ andP₂. The block 360 fractional radiation delivery continues to followafter partially completing the block 350 fractional optimization asdiscussed herein for blocks 150, 160. In embodiments where the angularsize of imaging portion P₂ corresponds to the angular difference betweenthe various beam orientations of the beam arrangement, the block 350fractional optimization and the block 360 fractional radiation deliverymay be performed for all of the beams at a particular beam orientationafter completion of the second imaging portion P₂ of the block 340fractional imaging process, although this is not necessary.

The process described above for imaging portions P₁, P₂ of the block 340fractional imaging process (together with the relevant portions of theblock 350 fractional optimization and the block 360 fractional radiationdelivery) may be repeated for the remaining image portions P₃, . . .P_(m).

A particular embodiment of the temporally overlapping fractional imaging(block 340), fractional optimization (block 350) and fractionalradiation delivery (block 360) is shown schematically as method 370 ofFIG. 8. Method 370 commences with the start of the block 340 fractionalimaging process. In block 342, image data is obtained for the firstportion P₁. As discussed above, the block 342 acquisition of image datamay comprise acquiring a plurality of two-dimensional projections.Method 370 then proceeds to block 343 which involves determining areconstructed three-dimensional image using the image data obtained fromthe portion P₁.

Once a reconstructed three-dimensional image is determined in block 343,method 370 may proceed to collect image data from the second portion P₂(block 344), determine a reconstructed three-dimensional image whichincorporates the image date acquired in portions P₁ and P₂ (block 345).Method 370 may continue in this manner to collect image data untilportion P_(m) (block 346). When the three-dimensional image isreconstructed from the image data in portions P₁, P₂, . . . P_(m) (block347), the block 340 fractional image acquisition is complete.

Once the first reconstructed three-dimensional image is determined inblock 343, the block 350 optimization can also commence by optimizingthe radiation delivery variables for all n beams in block 372. In theillustrated embodiment, the block 350 fractional optimization checksperiodically as to whether there is updated three-dimensional image dataavailable (block 373). If there is new three-dimensional image dataavailable (block 373 YES output), then method 370 updates the image data(block 375) and proceeds as discussed herein for method 170. Theprocedure of checking for updated three-dimensional image data may beperformed periodically as beams are removed from the optimizationprocess—see, for example, blocks 387 and 389 of the illustratedembodiment. In other respects, the block 350 optimization and the block360 radiation delivery of method 370 are similar to the block 150optimization and the block 160 radiation delivery of method 170.

The particular embodiments described above are applied to DAO radiationtreatment and therefore make use of treatment plan parameters andradiation delivery variables that are used in DAO radiation treatment(e.g. the beam arrangement, MLC leaf positions, MLC orientation and beamweight). In general, the invention described herein may be applied toother techniques of radiation treatment which involve differentradiation plan parameters and different radiation delivery variables.For example, in some beamlet-based radiation treatment techniques,radiation delivered from each particular beam orientation is broken downinto portions (referred to as “beamlets”) and the weights of thebeamlets are optimized for all the beam orientations in attempt toachieve the objectives of the radiation treatment plan. Thus the beamletweights may be the radiation delivery variables optimized in the block110 initial optimization and/or the block 150 fractional optimization.

In such embodiments, once the beamlet weights are optimized for aparticular beam orientation, a number of sets of MLC leaf positions andassociated beam weights can be derived (on the basis of the optimizedbeamlet weights) to deliver the optimized beamlets from the particularbeam orientation—i.e. the beam orientations represent the control pointsof beamlet-based radiation treatment. Each set of MLC leaf positions andone associated beam weight correspond to one individual beam from theparticular beam orientation. It may be necessary (or desirable) toprovide a plurality of individual beams from the particular beamorientation in order to deliver the optimized beamlet weights for thatparticular beam orientation. The block 150 fractional optimization mayinvolve optimizing the plurality of beamlet weights for a particularbeam orientation before deriving the individual beam parameters andpermitting the block 160 radiation delivery for that beam orientation(e.g. the periods T₁, T₂, . . . T_(n) discussed above, could correspondto the period for optimizing the beamlet weights for a particular beamorientation).

It will be appreciated that once the MLC leaf positions and individualbeam weights are derived from the optimized beamlets, it may bedesirable to use the MLC leaf positions and individual beam weights forfuture optimizations. In some embodiments, the block 110 initialoptimization may comprise optimizing a first set of radiation deliveryvariables (e.g. beamlet weights) and one or more of the block 150fractional optimizations may involve optimizing a second set ofradiation delivery variables (e.g. MLC leaf positions and individualbeam weights), wherein the second set of radiation delivery variablesmay be determined from the first set of radiation delivery variables.

In other radiation treatment techniques, the beam orientations and/orMLC leaf positions move dynamically while the radiation is beingdelivered. Non-limiting examples of dynamic delivery techniques includeTomotherapy, Dynamic Conformal Arc Therapy and Intensity Modulated ArcTherapy. In such embodiments, the radiation source is activated whilethe radiation delivery variables (e.g. MLC positions) are moving inbetween control points. For such embodiments, when the imaging system isan x-ray imaging system and the x-ray imaging system is integrated withthe radiation delivery apparatus the projections P₁, P₂, . . . P_(n) maybe obtained continuously and simultaneous to the radiation delivery. Inthis way, new projections can be acquired and used for reconstructionbetween each control point.

Certain implementations of the invention comprise computer processorswhich execute software instructions which cause the processors toperform a method of the invention. For example, one or more processorsin a dual modulation display system may implement data processing stepsin the methods described herein by executing software instructionsretrieved from a program memory accessible to the processors. Theinvention may also be provided in the form of a program product. Theprogram product may comprise any medium which carries a set ofcomputer-readable instructions which, when executed by a data processor,cause the data processor to execute a method of the invention. Programproducts according to the invention may be in any of a wide variety offorms. The program product may comprise, for example, physical mediasuch as magnetic data storage media including floppy diskettes, harddisk drives, optical data storage media including CD ROMs, DVDs,electronic data storage media including ROMs, flash RAM, or the like.The instructions may be present on the program product in encryptedand/or compressed formats.

Where a component (e.g. a software module, processor, assembly, device,circuit, etc.) is referred to above, unless otherwise indicated,reference to that component (including a reference to a “means”) shouldbe interpreted as including as equivalents of that component anycomponent which performs the function of the described component (i.e.that is functionally equivalent), including components which are notstructurally equivalent to the disclosed structure which performs thefunction in the illustrated exemplary embodiments of the invention.

As will be apparent to those skilled in the art in the light of theforegoing disclosure, many alterations and modifications are possible inthe practice of this invention without departing from the spirit orscope thereof. For example:

-   -   In the literature relating to radiation treatment, the target        volume may be referred to as the planning target volume (PTV).        The planning target volume is typically larger than the gross        target volume (GTV), which represents the exact image volume of        the target and the clinical target volume (CTV) which typically        includes a volume around the GTV where microscopic amounts of        disease may have spread. In this description, the phrase “target        volume” should be meant to include the PTV, GTV and/or the CTV        as the particular context may warrant.    -   While radiation treatment system 200 (FIG. 4) represents a        particular type of radiation delivery apparatus in conjunction        with which the invention may be implemented, it should be        understood that the invention may be implemented on different        radiation delivery apparatus, the components of which may differ        from those of radiation treatment system 200.    -   As discussed above, MLC 33 (FIG. 4) represents one beam-shaping        device which may be incorporated into radiation treatment system        200 and used to implement radiation treatment method 100. It        will be appreciated that there are a large number of variations        to MLC 33 which may be used in alternative embodiments. MLCs can        differ in design details, such as the number of leaves 36, the        widths of leaves 36, the shapes of the ends and edges of leaves        36, the range of positions that any leaf 36 can have,        constraints on the position of one leaf 36 imposed by the        positions of other leaves 36, the mechanical design of the MLC,        and the like. The invention described herein should be        understood to accommodate any type of configurable beam-shaping        apparatus 33 including MLCs having these and other design        variations.    -   In the embodiment described above, the MLC leaf positions and        orientations are initialized in block 120 such that the shapes        of the resultant beams match a projection of the target volume        (e.g. to approximate a beam's eye view outline of the target        volume) and the beam weights are initialized in block 120 to        have equal values which may be set so that the mean dose in the        target volume will equal a prescribed dose objective. In other        embodiments, other initialization schemes may be used for the        DAO parameters/radiation deliver variables. By way of        non-limiting example, the MLC leaf positions may be initialized        such that the resultant beams match a boolean projection of the        target volume minus the projection(s) of selected healthy        tissue/organs.    -   In the embodiments discussed above, the radiation delivery        variables (e.g. the DOA parameters varied during optimization)        include the positions of the MLC leaves 36 for each beam and the        weight of each beam. As mentioned briefly above, other DAO        parameters, such as the orientation of MLC 33 about axis 37 and        the characteristics of the beam arrangement (e.g. relative        orientations of the radiation source and the subject and the        number of beams in each such relative orientation), may be        additional or alternative radiation delivery variables. In other        radiation treatment techniques, the radiation delivery variables        optimized during the block 110 initial optimization and the        block 140 fractional optimization may be completely different        radiation delivery variables from those described above. Such        radiation delivery variables may be particular to the different        radiation treatment techniques. In some embodiments, the        radiation delivery variables used in the block 110 initial        optimization may be different than the radiation delivery        variables used in one or more of the block 150 fractional        optimizations. In such embodiments, the different radiation        delivery variables used in the one or more block 150 fractional        optimizations may be derived from the radiation delivery        variables used in the block 110 initial optimization.    -   The description set out above describes optimizing a radiation        delivery variables to minimize cost functions. It will be        appreciated by those skilled in the art that the optimized set        of radiation delivery variables need not strictly coincide with        the minimum of the cost function and that the optimized set of        radiation variables may comprise a clinically acceptable set of        radiation delivery variables which deviate from the absolute        minimum of the cost function.    -   In method 300 of FIG. 7, portions of all three of the block 340        fractional imaging process, the block 350 fractional        optimization process and the block 360 fractional radiation        delivery process overlap temporally. This is not necessary. In        some embodiments, it may be possible for portions of the block        340 fractional imaging process and the block 350 fractional        optimization process to overlap temporally, while the block 360        fractional radiation delivery occurs serially after the        completion of the block 350 fractional optimization process.    -   Portions P₁, P₂ . . . P_(m) of method 300 described above are        described as angular portions. It is not necessary that portions        P₁, P₂ . . . P_(m) be defined by their angular size. In some        embodiments, P₁, P₂ . . . P_(m) may be defined temporally, by        three-dimensional image reconstruction parameters or otherwise.    -   In the embodiments described above, three-dimensional images are        reconstructed from image data obtained from the most current        angular portion and any preceding angular portions. This is not        necessary. In some embodiments, only the most recent image data        from the most recent angular portion is used to reconstruct the        three-dimensional image.    -   In the embodiments described above, fractional imaging commences        at least slightly prior to fractional optimization and        fractional optimization commences at least slightly prior to        fractional radiation delivery. This is not necessary. In some        embodiments, delivery can commence at any time using initial        image data and initial optimized radiation delivery variables        until such time as new fractional image data and updated        fractional radiation delivery variables become available. When        fractional image data becomes available, then fractional        optimization can commence to obtain fractional updates to the        radiation delivery variables. When fractional updates for the        radiation delivery variables are available, these fractional        updates can be incorporated into the fractional radiation        delivery.

1. A method for radiation treatment of a subject involving one or morefractional treatments, the method comprising, for at least onefractional treatment: obtaining fractional image data pertaining to aregion of interest of the subject; performing a fractional optimizationof a radiation treatment plan to determine optimized values of one ormore radiation delivery variables, the fractional optimization based atleast in part on the fractional image data; and delivering a fraction ofthe radiation treatment plan to the region of interest using theoptimized values of the one or more radiation delivery variables as oneor more corresponding parameters of the radiation treatment plan;wherein a portion of performing the fractional optimization overlapstemporally with a portion of at least one of: obtaining the fractionalimage data and delivering the fraction of the radiation treatment plan.2. A method according to claim 1 wherein performing the fractionaloptimization comprises varying values of the one or more radiationdelivery variables so as to minimize a cost function to at least aclinically acceptable level and wherein the region of interest comprisesa target volume and at least some tissue from a region outside of thetarget volume and wherein the cost function increases when at least oneof: a radiation dose estimated to be delivered to the target volume isbelow a minimum target threshold (D_(min)) or above a maximum targetthreshold (D_(max)); and a radiation dose estimated to be delivered to aregion of healthy tissue outside of the target volume is above a maximumnon-target threshold (D₁).
 3. A method according to claim 1 comprisingdeveloping an initial radiation treatment plan prior to performing theat least one fractional treatment.
 4. A method according to claim 3wherein developing the initial radiation treatment plan comprises:obtaining initial image data pertaining to the region of interest of thesubject; and performing initial optimization of the initial radiationtreatment plan to determine optimized initial values of one or moreinitial variables, the initial optimization based at least in part onthe initial image data.
 5. A method according to claim 4 wherein the oneor more radiation delivery variables are the same as the one or moreinitial variables.
 6. A method according to claim 5 wherein the at leastone fractional treatment comprises a plurality of fractional treatments.7. A method according to claim 6 wherein performing the fractionaloptimization in the first of the plurality of fractional treatmentscomprises using the optimized initial values as fractional initialvalues of the one or more radiation delivery variables for performingthe fractional optimization in the first of the plurality of fractionaltreatments.
 8. A method according to claim 7 wherein performing thefractional optimization in the second and subsequent ones of theplurality of fractional treatments comprises using the optimized initialvalues as fractional initial values of the one or more radiationdelivery variables for performing the fractional optimization in thesecond and subsequent ones of the plurality of fractional treatments. 9.A method according to claim 7 wherein performing the fractionaloptimization in the second and subsequent ones of the plurality offractional treatments involves using the optimized values of the one ormore radiation delivery variables from a preceding one of the pluralityof fractional treatments as fractional initial values of the one or moreradiation delivery variables for performing the fractional optimizationin the second and subsequent ones of the plurality of fractionaltreatments.
 10. A method according to claim 6 wherein performing theinitial optimization comprises using a first optimization technique andperforming the fractional optimization comprises using a secondoptimization technique and wherein the first and second optimizationtechniques differ from one another.
 11. A method according to claim 10wherein the first optimization technique is relatively more capable ofovercoming local cost function minima than the second optimizationtechnique.
 12. A method according to claim 10 wherein the firstoptimization technique consumes a relatively large amount ofcomputational resources to complete an optimization process whencompared to the second optimization technique.
 13. A method according toclaim 10 wherein the first optimization technique comprises occasionallypermitting variations of the values of the one or more radiationdelivery variables which result in cost function increases and whereinthe second optimization technique comprises rejecting variations of thevalues of the one or more radiation delivery variables which result incost function increases.
 14. A method according to claim 10 wherein thefirst optimization technique comprises iteratively varying the values ofthe one or more radiation delivery variables in a random order and thesecond optimization technique comprises iteratively varying the valuesof the one or more radiation delivery variables in a specific order. 15.A method according to claim 10 wherein the second optimization techniquecomprises a constraint of a maximum inter-iteration variation of thevalues of the one or more radiation delivery variables betweensuccessive iterations.
 16. A method according to claim 15 wherein thefirst optimization technique also comprises a constraint of a maximuminter-iteration variation of the values of the one or more radiationdelivery variables between successive iterations and wherein the maximuminter-iteration variation associated with the second optimizationtechnique is less than the maximum inter-iteration variation associatedwith the first optimization technique.
 17. A method according to claim10 wherein the second optimization technique comprises one or moreconstraints which comprise maximum aggregate changes for the values ofthe one or more radiation delivery variables between their initialvalues and their optimized values.
 18. A method according to claim 10wherein the second optimization technique comprises, after selecting aradiation delivery variable to vary for a particular iteration, varyingthe selected radiation delivery variable in a direction according to: ifa direction of the variation of a previously varied radiation deliveryvariable resulted in a decrease in the cost function, then varying theselected radiation delivery variable in the same direction as thevariation of the previously varied radiation delivery variable; and ifthe direction of the variation of the previously varied radiationdelivery variable resulted in an increase in the cost function, thevarying the selected radiation delivery variable in an oppositedirection to the variation of the previously varied radiation deliveryvariable.
 19. A method according to claim 1 wherein portions ofperforming the fractional optimization and delivering the fraction ofthe radiation treatment plan overlap temporally.
 20. A method accordingto claim 1 wherein portions of obtaining fractional image data andperforming the fractional optimization overlap temporally.
 21. A methodfor radiation treatment of a subject involving one or more fractionaltreatments, the method comprising, for at least one fractionaltreatment: obtaining fractional image data pertaining to a region ofinterest of the subject; performing a fractional optimization of aradiation treatment plan to determine optimized values of one or moreradiation delivery variables, the fractional optimization based at leastin part on the fractional image data; and delivering a fraction of theradiation treatment plan to the region of interest using the optimizedvalues of the one or more radiation delivery variables as one or morecorresponding parameters of the radiation treatment plan; whereinportions of performing the fractional optimization and delivering thefraction of the radiation treatment plan overlap temporally; and whereinperforming the fractional optimization and delivering the fraction ofthe radiation treatment plan comprise: (a) optimizing values of the oneor more radiation delivery variables corresponding to each of a group ofcontrol points for an optimization period; (b) after the optimizationperiod: (i) fixing the values of the one or more radiation deliveryvariables corresponding to one of the control points to be the optimizedvalues of the one or more radiation delivery variables corresponding tothe one of the control points; (ii) permitting delivery of radiationcorresponding to the one of the control points using the optimizedvalues of the one or more radiation delivery variables corresponding tothe one of the control points; and (iii) removing the one of the controlpoints from the group of control points.
 22. A method according to claim21 comprising iteratively repeating steps (a), (b)(i), b(ii) and(b)(iii) of claim
 21. 23. A method according to claim 22 wherein eachcontrol point in the group of control points corresponds to anindividual radiation beam in a direct aperture optimization (DAO)radiation treatment process.
 24. A method according to claim 22 whereinthe one or more radiation delivery variables corresponding to eachcontrol point comprise one or more of: a weight of a beam in a directaperture optimization (DAO) radiation treatment process; positions ofmulti-leaf collimator (MLC) leaves for a beam in a DAO radiationtreatment process; and a pivotal orientation of a MLC for a beam in aDAO radiation treatment process.
 25. A method according to claim 22wherein each control point in the group of control points corresponds toa beam orientation in a beamlet-based radiation treatment process.
 26. Amethod according to claim 25 wherein the one or more radiation deliveryvariables corresponding to each control point comprise weights ofbeamlets for the beam orientation corresponding to the control point.27. A method according to claim 26 wherein the one or more radiationdelivery variables corresponding to each control point comprise: aweight of a particular beam; positions of multi-leaf collimator (MLC)leaves for a particular beam; and a pivotal orientation of a MLC for aparticular beam.
 28. A method according to claim 22 wherein theoptimization period comprises, for each repetition of steps (a), (b)(i),b(ii) and (b)(iii), at least one of: a threshold number of iterations ofthe step (a) optimization; a threshold time; achievement of a thresholdlevel of optimization as determined by evaluation of a cost function;and achievement of a threshold rate of change of optimization asdetermined by evaluation of a rate of change of a cost function.
 29. Amethod according to claim 28 wherein the optimization period is the samefor each repetition of steps (a), (b)(i), b(ii) and (b)(iii).
 30. Amethod according to claim 28 wherein the optimization period isdifferent for at least two repetitions of steps (a), (b)(i), b(ii) and(b)(iii).
 31. A method according to claim 22 comprising, after eachiteration of step (b)(ii), delivering radiation corresponding to the oneof the control points using the optimized values of the one or moreradiation delivery variables corresponding to the one of the controlpoints.
 32. A method according to claim 31 wherein the optimizationperiod for each of the second and subsequent repetitions of steps (a),(b)(i), (b)(ii), (b)(iii) is less than or equal to the time fordelivering radiation corresponding to the control point removed from thegroup of control points in the previous iteration.
 33. A methodaccording to claim 31 wherein the optimization period for each of thesecond and subsequent repetitions of steps (a), (b)(i), (b)(ii), (b)(iii) is set at the time required for delivering radiation correspondingto the control point removed from the group of control points in theprevious iteration.
 34. A method according to claim 21 comprisingiteratively repeating steps (a), (b)(i), (b)(ii) and (b)(iii) of claim21 until all of the control points have been removed from the group ofcontrol points.
 35. A method for radiation treatment of a subjectinvolving one or more fractional treatments, the method comprising, forat least one fractional treatment: obtaining fractional image datapertaining to a region of interest of the subject; performing afractional optimization of a radiation treatment plan to determineoptimized values of one or more radiation delivery variables, thefractional optimization based at least in part on the fractional imagedata; and delivering a fraction of the radiation treatment plan to theregion of interest using the optimized values of the one or moreradiation delivery variables as one or more corresponding parameters ofthe radiation treatment plan; wherein portions of obtaining fractionalimage data and performing the fractional optimization overlaptemporally; and wherein obtaining fractional image data comprisessuccessively obtaining a plurality of image data portions and, afterobtaining each image data portion, reconstructing a correspondingthree-dimensional representation of the region of interest based, atleast in part, on the image data portion.
 36. A method according toclaim 35 wherein reconstructing the corresponding three-dimensionalrepresentation of the region of interest is based, at least in part, ona combination of the image data portion and any preceding image dataportions.
 37. A method according to claim 35 wherein successivelyobtaining a plurality of image data portions comprises, for each imagedata portion, obtaining one or more corresponding two-dimensionalprojections from a corresponding angular region around the subject. 38.A method according to claim 37 wherein each corresponding angular regionis of substantially the same size.
 39. A method according to claim 37wherein a first corresponding angular region is larger than successiveangular regions.
 40. A method according to claim 37 wherein deliveringthe fraction of the radiation treatment plan to the region of interestcomprises delivering radiation from a plurality of beam orientations andwherein the corresponding angular regions correspond to a spacingbetween the beam orientations.
 41. A method according to claim 35comprising, after obtaining a first image data portion andreconstructing a first three-dimensional representation of the region ofinterest, commencing the fractional optimization using the firstthree-dimensional representation of the region of interest prior tocompleting obtaining a second image data portion.
 42. A method accordingto claim 41 wherein reconstructing the first three-dimensionalrepresentation of the region of interest is based at least in part oninitial image data obtained prior to obtaining the first image dataportion.
 43. A method according to claim 42 comprising, after obtainingthe second image data portion and reconstructing a secondthree-dimensional representation of the region of interest, continuingthe fractional optimization using the second three-dimensionalrepresentation of the region of interest prior to completing obtaining athird image data portion.
 44. A method according to claim 35 whereineach of the image data portions are of substantially the same size. 45.A method according to claim 35 wherein a first image data portion islarger than successive image data portions.
 46. A method according toclaim 35 wherein portions of performing the fractional optimization anddelivering the fraction of the radiation treatment plan overlaptemporally.
 47. A method according to claim 46 wherein performing thefractional optimization and delivering the fraction of the radiationtreatment plan comprise: (a) optimizing values of the one or moreradiation delivery variables corresponding to each of a group of controlpoints for an optimization period; (b) after the optimization period:(i) fixing the values of the one or more radiation delivery variablescorresponding to one of the control points to be the optimized values ofthe one or more radiation delivery variables corresponding to the one ofthe control points; (ii) permitting delivery of radiation correspondingto the one of the control points using the optimized values of the oneor more radiation delivery variables corresponding to the one of thecontrol points; and (iii) removing the one of the control points fromthe group of control points.
 48. A method according to claim 47comprising iteratively repeating steps (a), (b)(i), b(ii) and (b)(iii)of claim
 48. 49. A method for radiation treatment of a subject involvingone or more fractional treatments, the method comprising: obtaininginitial image data pertaining to a region of interest of the subject;performing initial optimization of an initial radiation treatment planto determine optimized initial values of one or more initial variables,the initial optimization based at least in part on the initial imagedata; and for at least one fractional treatment: obtaining fractionalimage data pertaining to the region of interest of the subject;performing a fractional optimization of a radiation treatment plan todetermine optimized values of one or more radiation delivery variables,the fractional optimization based at least in part on the fractionalimage data; and delivering a fraction of the radiation treatment plan tothe region of interest using the optimized values of the one or moreradiation delivery variables as one or more corresponding parameters ofthe radiation treatment plan; wherein performing the initialoptimization comprises using a first optimization technique andperforming the fractional optimization comprises using a secondoptimization technique and wherein the first and second optimizationtechniques differ from one another.
 50. A method according to claim 49wherein the first optimization technique is relatively more capable ofovercoming local cost function minima than the second optimizationtechnique.
 51. A method according to claim 49 wherein the firstoptimization technique consumes a relatively large amount ofcomputational resources to complete an optimization process whencompared to the second optimization technique.
 52. A method according toclaim 49 wherein the first optimization technique comprises occasionallypermitting variations of the values of the one or more radiationdelivery variables which result in cost function increases and whereinthe second optimization technique comprises rejecting variations of thevalues of the one or more radiation delivery variables which result incost function increases.
 53. A method according to claim 49 wherein thefirst optimization technique comprises iteratively varying the values ofthe one or more radiation delivery variables in a random order and thesecond optimization technique comprises iteratively varying the valuesof the one or more radiation delivery variables in a specific order. 54.A method according to claim 49 wherein the second optimization techniquecomprises a constraint of a maximum inter-iteration variation of thevalues of the one or more radiation delivery variables betweensuccessive iterations.
 55. A method according to claim 54 wherein thefirst optimization technique also comprises a constraint of a maximuminter-iteration variation of the values of the one or more radiationdelivery variables between successive iterations and wherein the maximuminter-iteration variation associated with the second optimizationtechnique is less than the maximum inter-iteration variation associatedwith the first optimization technique.
 56. A method according to claim49 wherein the second optimization technique comprises one or moreconstraints which comprise maximum aggregate changes for the values ofthe one or more radiation delivery variables between their initialvalues and their optimized values.
 57. A method according to claim 49wherein the second optimization technique comprises, after selecting aradiation delivery variable to vary for a particular iteration, varyingthe selected radiation delivery variable in a direction according to: ifa direction of the variation of a previously varied radiation deliveryvariable resulted in a decrease in the cost function, then varying theselected radiation delivery variable in the same direction as thevariation of the previously varied radiation delivery variable; and ifthe direction of the variation of the previously varied radiationdelivery variable resulted in an increase in the cost function, thevarying the selected radiation delivery variable in an oppositedirection to the variation of the previously varied radiation deliveryvariable.
 58. A computer program product comprising a non-transitorycomputer readable medium encoded with code segments for controlling aradiation treatment system to provide one or more fractional radiationtreatments to a subject, the radiation treatment system comprising animaging system and a radiation delivery system, the code segmentsconfigured to direct one or more processors, for at least one fraction,to: cause the imaging system to obtain fractional image data pertainingto a region of interest of the subject; perform a fractionaloptimization of a radiation treatment plan to determine optimized valuesof one or more radiation delivery variables, the fractional optimizationbased at least in part on the fractional image data; and cause theradiation delivery system to deliver a fraction of the radiationtreatment plan to the region of interest using the optimized values ofthe one or more radiation delivery variables as one or morecorresponding parameters of the radiation treatment plan; wherein aportion of performing the fractional optimization overlaps temporallywith a portion of at least one of: obtaining the fractional image dataand delivering the fraction of the radiation treatment plan.
 59. Acomputer program product comprising a non-transitory computer readablemedium encoded with code segments for controlling a radiation treatmentsystem to provide one or more fractional radiation treatments to asubject, the one or more fractional radiation treatments occurring afterobtaining initial image data pertaining to a region of interest of thesubject and performing initial optimization of an initial radiationtreatment plan to determine optimized initial values of one or moreinitial variables based at least in part on the initial image data, theradiation treatment system comprising an imaging system and a radiationdelivery system, the code segments configured to direct one or moreprocessors, for at least one fraction, to: cause the imaging system toobtain fractional image data pertaining to the region of interest of thesubject; perform a fractional optimization of a radiation treatment planto determine optimized values of one or more radiation deliveryvariables, the fractional optimization based at least in part on thefractional image data; and cause the radiation delivery system todeliver a fraction of the radiation treatment plan to the region ofinterest using the optimized values of the one or more radiationdelivery variables as one or more corresponding parameters of theradiation treatment plan; wherein performing the initial optimizationcomprises using a first optimization technique and performing thefractional optimization comprises using a second optimization techniqueand wherein the first and second optimization techniques differ from oneanother.
 60. A radiation treatment system for providing one or morefractional radiation treatments to a subject, the radiation treatmentsystem comprising: an imaging system for obtaining images of a region ofinterest of the subject; a radiation delivery system for deliveringradiation to the region of interest of the subject; a controllerconnected to the imaging system and to the radiation delivery system andconfigured, for at least one fraction, to: cause the imaging system toobtain fractional image data pertaining to the region of interest of thesubject; perform a fractional optimization of a radiation treatment planto determine optimized values of one or more radiation deliveryvariables, the fractional optimization based at least in part on thefractional image data; and cause the radiation delivery system todeliver a fraction of the radiation treatment plan to the region ofinterest using the optimized values of the one or more radiationdelivery variables as one or more corresponding parameters of theradiation treatment plan; wherein a portion of performing the fractionaloptimization overlaps temporally with a portion of at least one of:obtaining the fractional image data and delivering the fraction of theradiation treatment plan.
 61. A radiation treatment system for providingone or more fractional radiation treatments to a subject, the one ormore fractional radiation treatments occurring after obtaining initialimage data pertaining to a region of interest of the subject andperforming initial optimization of an initial radiation treatment planto determine optimized initial values of one or more initial variablesbased at least in part on the initial image data, the radiationtreatment system comprising: an imaging system for obtaining images of aregion of interest of the subject; a radiation delivery system fordelivering radiation to the region of interest of the subject; acontroller connected to the imaging system and to the radiation deliverysystem and configured, for at least one fraction, to: cause the imagingsystem to obtain fractional image data pertaining to the region ofinterest of the subject; perform a fractional optimization of aradiation treatment plan to determine optimized values of one or moreradiation delivery variables, the fractional optimization based at leastin part on the fractional image data; and cause the radiation deliverysystem to deliver a fraction of the radiation treatment plan to theregion of interest using the optimized values of the one or moreradiation delivery variables as one or more corresponding parameters ofthe radiation treatment plan; wherein performing the initialoptimization comprises using a first optimization technique andperforming the fractional optimization comprises using a secondoptimization technique and wherein the first and second optimizationtechniques differ from one another.